Differential DSP: An audio toolbox for end-to-end ml
Zhao, An
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https://hdl.handle.net/2142/109453
Description
Title
Differential DSP: An audio toolbox for end-to-end ml
Author(s)
Zhao, An
Issue Date
2020-12-10
Director of Research (if dissertation) or Advisor (if thesis)
Smaragdis, Paris
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Differential
DSP
DDSP
Machine learning
Audio
Classification
Abstract
The short-time Fourier transform (STFT) has been a staple of signal processing, often being the first step for many audio tasks. A very familiar process when using the STFT is the search for the best STFT parameters, as they often have significant side effects if chosen poorly. These parameters are often de ned in terms of an integer number of samples, which makes their optimization non-trivial. We present a toolbox that allows us to obtain gradients for commonly used audio filter parameters, and for STFT parameters with respect to arbitrary cost functions, thus enabling gradient descent optimization of quantities like the STFT window length or the STFT hop size. We do so for parameter values that stay constant throughout an input, but also for cases where these parameters have to dynamically change over time to accommodate varying signal characteristics.
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